Date: 2026-04-10 Analyst: nemesis Classification: Trojan / Backdoor (Alien RAT variant) Severity: CRITICAL Campaign ID: CityOfSin (extracted from C2 callback UTM parameters) Scope: CPUID official domain compromise affecting CPU-Z, HWMonitor, HWMonitor Pro, PerfMonitor 2, powerMAX + separately FileZilla Status: Breach confirmed and fixed by CPUID; site was compromised ~6 hours on April 9-10, 2026 CPUID Statement: "A secondary feature (a side API) was compromised for approximately six hours [...] causing the main website to randomly display malicious links. Our signed original files were not compromised."

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Before we look at some common commands, I just want to note a few keyboard commands that are very helpful:

• Up Arrow: Will show your last command
• Down Arrow: Will show your next command
• Tab: Will auto-complete your command
• Ctrl + L: Will clear the screen